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International Journal of Human–Computer Interaction
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/hihc20
Beyond Pleasure, Desire for Meaningful
Consumption and Peacefulness from Digital
Entertainment Platforms; Extending UTAUT2
Model with Eudemonic Motivation and Tranquility
Mary Kuriakose & Gopalan Nagasubramaniyan
To cite this article: Mary Kuriakose & Gopalan Nagasubramaniyan (24 Jan 2024): Beyond
Pleasure, Desire for Meaningful Consumption and Peacefulness from Digital Entertainment
Platforms; Extending UTAUT2 Model with Eudemonic Motivation and Tranquility, International
Journal of Human–Computer Interaction, DOI: 10.1080/10447318.2024.2305479
To link to this article: https://doi.org/10.1080/10447318.2024.2305479
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Beyond Pleasure, Desire for Meaningful Consumption and Peacefulness from
Digital Entertainment Platforms; Extending UTAUT2 Model with Eudemonic
Motivation and Tranquility
Mary Kuriakose and Gopalan Nagasubramaniyan
Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, India
ABSTRACT
The landscape of entertainment consumption has undergone a profound shift, with the emer-
gence of digital entertainment platforms becoming indispensable in satisfying consumers’ desire
for convenience. This study examines the complex interaction between user motivations and the
adoption of digital entertainment platforms, focusing on the desire for meaningful consumption
and the pursuit of experiencing peacefulness by adopting digital entertainment platforms. We
extend the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) by introducing eude-
monic motivation (EM) and tranquility (TL) as constructs influencing users’ behavioral intention (BI)
and actual use (AU) of digital entertainment platforms. Adopting a non-probability mixed sam-
pling method consisting of convenience and snowball sampling, we collected data from 870
active users across five southern Indian states through online surveys. A quantitative study design
was used to test the model by employing partial least squares–structural equation modeling (PLS-
SEM). The result of PLS-SEM analysis indicated that BI is positively influenced by effort expectancy
(EE), hedonic motivation (HM), habit (HA), EM, and TL. Social influence (SI) has no significant influ-
ence on BI. The findings reveal that consumers seek meaningful eudemonic experiences and a
sense of peacefulness from their digital entertainment interactions. This study serves as a founda-
tion for future research, emphasizing the significance on crafting technologies that resonate with
users’ quest for meaning and inner peace in digital entertainment consumption.
KEYWORDS
Digital entertainment
platforms; eudemonic
motivation; tranquility;
UTAUT2; meaningful
consumption; peacefulness
1. Introduction
Digital entertainment platforms are indispensable applica-
tions that satisfy the need for convenience in accessing
entertainment content without the time and location barriers
(Sridevi & Ajith, 2023). The evolution of entertainment
media platforms from traditional to modern formats, driven
by the trajectory of digital innovation and advancements in
big data transfer technologies, has seen the emergence of
digital platforms as primary substitutes for the recorded
viewing of linear TV, encompassing dramas and movies
(Yamatsu & Lee, 2023). Examples of digital entertainment
platforms include social media platforms like Facebook and
Instagram, ad-supported websites like YouTube, and con-
tent-sharing platforms like Netflix, and Disneyþ.
Traditional television’s incapacity to tailor content based
on users’ moods and emotional needs compels individuals
to switch from TV to digital entertainment platforms
(Katherine Chen, 2019). The attractive aspect of digital plat-
forms lies in their diverse content libraries that cover vari-
ous genres and allow consumers to enjoy content at their
convenience free from rigid TV schedules. Previous studies
on user behavior in digital entertainment affirm the pivotal
role of the content library in choosing digital platforms over
traditional television (Menon, 2022; Raj & Nair, 2021;
Sadana & Sharma, 2021; Uthaman & Faizal, 2021). The
desire for convenience arises when consumers try to achieve
specific consumption goals. The concept of the goal frame
pertains to the various categories of objectives within a
rational decision-making process. These categories encom-
pass hedonic goals associated with enjoyment and satisfac-
tion, normative goals representing social approval, and gain
goals focusing on the pursuit of value (Kallgren et al., 2000;
Lindenberg & Steg, 2007).
According to Kim et al. (2022), interactive recommenda-
tion agents (IRAs) provide personalized products with the
help of artificial intelligence (AI) to satisfy the goal-oriented
consumption behavior of modern consumers. Digital plat-
forms, specifically digital entertainment platforms such as
Netflix and Amazon Prime, deliver personalized content
using IRAs. Unlike traditional media platforms, digital plat-
forms have advanced specifications backed by technological
innovations (Katherine Chen, 2019). These specifications
make modern media platforms more desirable for entertain-
ment consumers.
The current body of literature offers valuable perspectives
on the utilitarian features of digital entertainment platforms
CONTACT Mary Kuriakose marykuriakose26@gmail.com Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli,
India
� 2024 Taylor & Francis Group, LLC
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION
https://doi.org/10.1080/10447318.2024.2305479
influencing consumers to opt for them, as well as the enjoy-
ment factors that contribute to users maintaining their sub-
scription commitments (Basuki et al., 2022; Bhattacharyya
et al., 2022; Chen et al., 2023; Madanaguli et al., 2021;
Mulla, 2022; Yoon & Kim, n.d.; Yousaf et al., 2021; Periaiya
& Nandukrishna, 2023). According to their findings, the
perceived utility and quality obtained from the digital plat-
forms create a trust in the consumers to make favorable
purchasing decisions. Also, gratifications such as modality,
pass time, escape, and personalization are significantly asso-
ciated with stickiness or repurchasing intentions.
However, a research gap emerges when it comes to
exploring the motivations behind the adoption of digital
entertainment technology, stemming from human behavior
driven by a quest for meaning and a state of mind devoid of
distress (Gupta, 2019; Voorhoeve, 2022). The experience of
meaningful consumption and a sense of inner peace goes
beyond mere pleasure and enjoyment. The pursuit of mean-
ing, purpose, and self-worth represent the eudemonic
dimension of emotional well-being, distinct from hedonic
motives (Sun et al., 2023). The desire for a serene mind free
from pain and stress, brings tranquility (TL) to the human
psyche (Voorhoeve, 2022). The current literature does not
adequately address whether users of digital platforms seek
eudemonic well-being and tranquil experiences from their
digital entertainment interactions. Comprehending the intri-
cate interplay between digital platforms and emotionally
driven humans requires acknowledging the inherent
irrationality of humans and their constant quest for meaning
and self-worth in their actions and consumption.
For instance, individuals utilizing the content library of
digital entertainment platforms do not exclusively seek content
for amusement. They may opt for movies or series with con-
templative, introspective, or meditative qualities when in a
mood for self-reflection, deliberation, or a search for life’s pur-
pose (Oliver & Hartmann, 2010). These eudemonic considera-
tions differ from hedonic benefits like pleasure and fun. The
viewer is not solely pursuing a jovial experience but rather
something transcending mere pleasure – an experience that
resonates with poignancy, tenderness, and the sensation of
being moved or touched at a deeper level (Beth Oliver, 2021).
Additionally, the collection of content brings a sense of
TL to its users (Karunakaran et al., 2023). Attaining a sense
of peacefulness through digital entertainment platforms
involves choosing content that matches one’s relaxation
preferences. Methods for improving the TL of the viewing
experience include selecting calming genres like nature doc-
umentaries, setting up a comfortable environment by adjust-
ing lighting and eliminating distractions, practicing focused
viewing without multitasking, opting for content featuring
nature or picturesque scenery, exploring meditative content,
avoiding overly stimulating shows, curating a personalized
playlist of soothing content, and scheduling dedicated times
for deliberate and mindful viewing (Beth Oliver, 2021;
Oliver, 2022). The subjective nature of what induces peace-
fulness emphasizes the significance of recognizing content
types that resonate with individual preferences, whether it
be through nature documentaries, soothing music, or
mindful storytelling, to personalize the viewing experience
for heightened serenity and relaxation (Hyun Yoon & Ku
Kim, 2023; Pan & Cho, 2022; Sridevi & Ajith, 2023).
A scientific investigation into whether digital entertainment
platforms can fulfill the psychological need for meaningful
consumption and tranquillity surpassing mere pleasure, has
the potential to fill the gap in our comprehension of the
interaction between humans and technology in the realm of
entertainment content consumption. Specifically, this study
presents the results of an investigation designed to expand the
research on psychological and subjective well-being related to
the experiences provided by digital platforms which viewers
identify as particularly meaningful and peaceful. Delving into
psychological elements aids in crafting technologies that are in
harmony with users’ requirements, inclinations, and behaviors.
A deepened understanding of consumer behavior toward
digital entertainment platforms enables businesses to formulate
strategies that promote technology adoption and ensure sus-
tained user satisfaction over the long term.
For a considerable period, academic research has exten-
sively examined the adoption and acceptance of technologies
by users across diverse sectors, including healthcare, educa-
tion, corporate, government, tourism, business, social net-
working, and supply chain (Abu-Shanab et al., 2024; Elshan
et al., 2022; Gursoy et al., 2019; Kaye et al., 2021; Papakostas
et al., 2022; Patnaik et al., 2022; Sagnier et al., 2020;
Tao et al., 2020). Researchers from different fields have been
experimenting with established theoretical models to under-
stand how users respond to and accept disruptive technolo-
gies. The Theory of Reasoned Action (TRA), Theory of
Planned Behavior (TPB), Social Cognitive Theory (SCT),
Diffusion of Innovation Theory (DOI), Perceived
Characteristics of Innovation Theory (PCIT), Motivational
Model (MM), Uses and Gratification Theory (U&G), and
the Unified Theory of Acceptance and Use of Technology
(UTAUT) are models widely employed by researchers in
their studies (Al-Emran & Grani�
c, 2021).
The previous research studying user acceptance of digital
entertainment platforms employed contemporary user accept-
ance models to assess consumers’ attitudes and behavioral
intentions to adopt digital media platforms (Camilleri &
Falzon, 2021; Chen et al., 2023; Mulla, 2022; A. Sharma et al.,
2023; Shim et al., 2022). This study adopted the extended
UTAUT2 proposed by Venkatesh et al. (2012). The initial
UTAUT model (Venkatesh et al. (2003) synthesized elements
from eight different models investigating user acceptance of
technology and constructed a unified model that integrates ele-
ments across the eight models. Unlike the initial version, which
dealt with the organizational outcomes connected to the
employment of new technology, the extended version of
UTAUT, i.e., UTAUT2, is intended for consumer research
about the user acceptance of technology. The effectiveness of
the UTAUT2 model in examining hedonic and eudemonic
motives arises from its thorough framework, empirical valid-
ation, predictive capabilities, and versatility across various con-
texts. This model offers a solid basis for investigating diverse
motivations behind technology adoption, covering pleasure-
centric and purpose-driven dimensions.
2 M. KURIAKOSE AND G. NAGASUBRAMANIYAN
This research contributes to the theoretical framework of
UTAUT2 by extending the model with eudemonic motivation
(EM) and TL. This extension addresses the difference between
consumers’ hedonic goals, which describe fun and enjoyment
received by the consumers by adopting a new entertainment
media technology, and the eudemonic aspects, such as mean-
ingfulness and a sense of serenity from that technology usage.
However, the study has limitations tied to its geographical
focus on the southern states of India and the exclusion of
moderating factors like age, gender, and experience integral to
the UTAUT2 model. Future research covering diverse regions
in India and incorporating cross-country comparisons is sug-
gested for a more comprehensive understanding of consumer
behavior toward digital entertainment platforms.
1.1. Digital entertainment platforms
The term “digital entertainment platforms” encompasses a wide
spectrum of online gaming platforms, over-the-top (OTT) serv-
ices, and video on demand (VOD) platforms (Eden & Ahn,
2018; Elkins, 2019; KPMG, 2019; Wescott et al., 2019). While
there are functional similarities between VOD platforms (such
as Google Play Movies & TV, Vudu) and OTT platforms (like
Netflix, Amazon Prime Video, Hulu, and Disneyþ), this study
opts for the term “digital entertainment platforms” over “OTT
platforms” due to the distinct focus of VOD platforms solely
on video streaming. Furthermore, both paid subscription mod-
els and free access plans are under examination.
Digital entertainment platforms have a well-expanding mar-
ket globally as well as domestically. The Compound Annual
Growth Rate (CAGR) over the projected period can drive the
global OTT platform market from USD 202.5 billion in 2022
to approximately USD 434.5 billion by 2027 (Menon, 2022).
This data signify a substantial expansion of about 16.5%. The
digital entertainment sector in India is currently in its early
stages, with television maintaining a dominant market presence
compared to digital platforms (EY, 2023).
2. Research model and hypothesis development
The initial UTAUT model has four constructs (predictors):
performance expectancy (PE), effort expectancy (EE), social
influence (SI), and facilitating conditions (FC) (Venkatesh
et al., 2003). The extended version of the initial model has
three additional predictors: hedonic motivation (HM), price
value (PV), and habit (HA) (Venkatesh et al., 2012). The
research employed UTAUT2 model for assessing user
behavior toward the adoption of digital entertainment plat-
forms, specifically OTT platforms, studied variables such as
price, content, flexibility, convenience (perceived ease of
use), perceived usefulness, perceived enjoyment (HM), desire
to be free from any constraints, entertainment value, social-
ization, cultural inclusion, binge-watching, favorable eco-
nomic position, system quality, service differentiation,
content quality, security conditions, self-efficacy, expenses
incurred on services convenience, frustration with TV, trial-
ability, and trendiness (Ajith & Periaiya, 2024; Barata &
Coelho, 2021; Bhattacharyya et al., 2022; Hallur et al., 2023;
Malewar & Bajaj, 2020; Raj & Nair, 2021; Sadana & Sharma,
2021; Shah & Mehta, 2022; Sridevi & Ajith, 2023; Uthaman
& Faizal, 2021). In this study, we extend the UTAUT2 with
two more predictors: EM (Ewert et al., 2020) and TL (Hu
et al., 2021), to assess the user behavior toward digital enter-
tainment platforms, especially OTT platforms. The primary
objective of extending the UTAUT2 model with these two
psychological factors is to assess how much consumers
aspire to engage in meaningful consumption while also seek-
ing a tranquil viewing experience. Figure 1, the proposed
research model is an extended version of the original
UTAUT2 model by Venkatesh et al. (2012).
Figure 1. Research model.
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 3
2.1. Effort expectancy (EE)
It refers to the perceived ease or difficulty of utilizing a spe-
cific technology or system. It represents the user’s expect-
ation of the effort needed to interact with and use the
technology (Venkatesh et al., 2003). One of the elements
influencing whether users would accept new technology is
its ease of use (Tsai & Lin, 2022). When users perceive tech-
nology as easy to use and require minimal effort, they are
more likely to adopt and accept it. Digital platforms provide
hassle-free entertainment through multi-device accessibility
(Barta et al., 2021). Therefore, when users perceive technol-
ogy as useful and easy to use, they are more motivated to
engage with it and achieve their desired goals. As a result,
the following outcomes arise from the information men-
tioned earlier:
H1a: EE positively impacts the behavioral intention to use
digital entertainment platforms.
H1b: EE has a positive indirect impact on the actual usage
of digital entertainment platforms
2.2. Social influence (SI)
The degree to which consumers believe that significant indi-
viduals (such as family and friends) think they should use a
certain technology is known as SI (Venkatesh et al., 2012).
The influence of social dynamics among peers and adher-
ence to societal norms significantly shapes individuals’ per-
spectives on embracing new technologies. If a technology
becomes widely accepted within a social circle, individuals
are frequently motivated to align with these prevailing
norms (Abdat, 2020; Al-Saedi et al., 2020; Daniali et al.,
2022; Wei et al., 2021; Zhou et al., 2019). Consumers are
expected to be influenced by the people around them while
adopting innovative technology for accessing entertainment.
The research on media and entertainment provides evidence
for this argument (Abdul Latiff et al., 2022; Chakraborty
et al., 2023; Sowmiya et al., 2022). Accordingly, our research
suggests the following:
H2a: SI has a positive impact on the behavioral intention to
use digital entertainment platforms
H2b: SI has a positive indirect effect on the actual usage of
digital entertainment platforms
2.3. Hedonic motivation (HM)
Hedonism describes the pleasure-seeking emotive aspects of
consumers’ experience with a product (Scarpi, 2020). The
traditional economic explanation for consumer behavior
revolves around a product or service’s functional and utili-
tarian properties (Loudon & Della Bitta, 1993). By exploring
the emotional aspects of consumption behavior, researchers
try to explain a consumer’s irrational, non-calculative deci-
sion-making process (Vrtana, 2021). Fun or pleasure experi-
enced when utilizing technology is a key factor in
determining whether or not a consumer would accept and
use it (Aranyossy, 2022). Recent studies that incorporated
HM agree with the relevance of HM in the behavioral inten-
tion to accept a new technology (Bhattacharyya et al., 2022;
Mulla, 2022; Soren & Chakraborty, 2024). Therefore, we
suggest the following:
H3a: HM positively influences the behavioral intention to
use digital entertainment platforms.
H3b: HM has a positive indirect influence on the actual use
(AU) of digital entertainment platforms
2.4. Eudemonic motivation (EM)
While HM stands for the pleasure-seeking behavior of an
individual, EM encourages a person to seek meaningfulness
beyond the pleasure experience (Huta & Waterman, 2014;
Kaczmarek, 2017). The eudemonic well-being is fulfilled
when an individual achieves growth, meaning, authenticity,
and excellence (Gupta, 2019). Pleasure is a fleeting emotion,
whereas growth and meaningfulness are deeply ingrained in
the mind. People attempt to make meaningful purchases
when considering options beyond the utilitarian value of the
good or service. For instance, travelers select locations that
satisfy their emotional, physical, and spiritual needs and
offer meaningful experiences (Curtin & Brown, 2019; Li
et al., 2021). A study conducted by Yen et al. (2022), focus-
ing on the relationship between social mobility beliefs and
well-being, suggests that happiness obtained through EM
significantly mediates the well-being effect of social mobility
beliefs. The evidence from leisure research also agrees that
consumers seek meaningfulness in their leisure activities
(O’Brien, 2023). Digitalization has significantly enhanced the
ease and customization of consumption. It raises the ques-
tion of whether digitalization also offers meaningful experi-
ences through consumption. Do consumers find meaning in
adopting technology to access entertainment? Researchers
exploring the emotional aspects of technology adoption have
started to consider emotional well-being to understand
pleasure-oriented consumption behaviors (Aspillaga &
Correa, 2022; Cachero-Mart�
ınez et al., 2023; Naguim &
Nfissi, 2023; Zambianchi, 2022). In this respect, we suggest:
H4a: EM positively influences the behavioral intention to
use digital entertainment platforms.
H4b: EM has a positive indirect effect on the AU of digital
entertainment platforms.
2.5. Tranquility (TL)
The literal meaning of TL is “the state of being quiet and
peaceful” (Oxford Learners dictionary, n.d.). In consumer
research, TL stands for the consumer’s desire for the feeling
of calm, peacefulness, and serenity. These feelings play a
crucial role in assisting consumers in managing the various
stresses they encounter in their physical, social, and emo-
tional lives (Jia & Wyer, 2019). Studies investigating the
4 M. KURIAKOSE AND G. NAGASUBRAMANIYAN
significance of happiness in consumer decision-making pro-
pose that the experience of happiness can take two forms:
either as a sense of excitement or a sense of TL, depending
on the temporal perspective during the decision-making
process (Mogilner et al., 2012). Varman and Belk (2008) in
their study, suggest that the evolution of consumer culture
among marginalized groups is characterized by dialectics
between turmoil and TL. Their findings align with the rec-
ommendations of the research regarding the relevance of
temporal experiences in shaping consumption practices
(Woermann & Rokka, 2015). The modern concept of using
technology, particularly embracing specific technologies to
attain a state of TL, revolves around whether individuals
utilize these tools as a means to escape stress and anxiety
and find a sense of calm and serenity (Chen et al., 2023;
Gabbiadini et al., 2021). Menon (2022), in his research,
found out that OTT platforms help users to relieve stress.
Another research study on the binge-watching behavior of
Pakistani teenagers by Qayyoum and Malik (2023) indicated
that the main reasons Pakistani teenagers turned to binge-
watching were entertainment, escape, and excitement. The
findings affirm that many people consider digital entertain-
ment platforms as a means to find intervals of peace and
calmness to recharge and rejuvenate. Therefore, we propose:
H5a: TL positively influences the behavioral intention to use
digital entertainment platforms.
H5b: TL has a positive indirect influence on the AU of
digital entertainment platforms.
2.6. Habit (HA)
HA has been described as the degree to which individuals
tend to engage in behaviors automatically due to learned
associations, often equating it with automaticity (Venkatesh
et al., 2012). HA can be a repeated behavior (Verplanken
et al., 2014). At the same time, not all repeated actions can
become a HA (Dai et al., 2020). Also, researchers argue that
when analyzing cross-sectional data and examining behav-
iors that have been repeatedly enacted in the past, it
becomes challenging to distinguish between past behavior,
HAs, and current beliefs and attitudes (Scholderer &
Trondsen, 2008). In consumer research, HA has a crucial
role as it predicts consumers’ buying behavior based on their
consumption patterns. This predictive capacity of HA makes
it a solid construct to measure consumer behavior toward
products and services. The research findings by Soren and
Chakraborty (2023) indicate that HA significantly predicts
users’ commitment toward hedonic apps like OTT plat-
forms. According to the findings of K. Sharma and
Lulandala (2023), consumers’ usage of OTT has shifted from
sporadic to habitual. The utilization of technology can
evolve into a deeply ingrained HA, making it difficult to
break away from. Additionally, the positive experiences
gained from using the technology may deter users from
reverting to their previous consumption choices. Therefore,
we suggest:
H6a: HA positively influences the behavioral intention to
use digital entertainment platforms.
H6b: HA has a positive indirect impact on the AU of digital
entertainment platforms.
2.7. Behavioral intention (BI) and actual use (AU)
The latent variables BI and AU measure consumers’ inten-
tion to accept technology and the actual usage behavior
(Venkatesh et al., 2012). These variables align with the TPB,
the TRA, and the Technology Acceptance Model (TAM)
(Venkatesh et al., 2003). Also, this research includes the
controlling effect of age, gender, and education on AU to
ensure the internal validity of the research design.
Therefore, we propose,
H7: Behavioral Intention positively affects the AU of digital
entertainment platforms.
3. Methods
3.1. Measurement of constructs
In this research, the measurement instruments employed to
gauge the variables, following the UTAUT2 model, were
derived from prior studies. This decision was made with the
aim of guaranteeing the accuracy and consistency of
the constructs. Table 1 shows the related studies from which
the items were adopted. The items are evaluated using a
five-point Likert scale, with one denoting “strongly disagree”
and five denoting “strongly agree” (Taherdoost, 2019;
Yonnie Chyung et al., 2018).
3.2. Sample and data collection
After distilling and recording the existing literature regard-
ing the various aspects of the consumer-technology adoption
process, an online survey approach has been implemented
(Braun et al., 2021; Cos¸kun & Dirlik, 2021) to collect data
Table 1. Constructs.
No. Construct Author No. of items
1 Effort expectancy Aranyossy (2022) 4
2 Social influence Aranyossy (2022), Ram
�
ırez-Correa et al. 2019) 4
3 Hedonic motivation Shaw and Sergueeva (2019) 4
4 Habit Nikolopoulou et al. (2020) 4
5 Eudemonic motivation Cachero-Mart
�
ınez et al. (2023), Gupta (2019) 4
6 Tranquility Gupta (2019) 4
7 Behavioral intention Aranyossy (2022), Nikolopoulou et al. (2020) 4
8 Actual use Chen et al. (2023) 4
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 5
from the active consumers of digital entertainment
platforms.
We selected the five southern states of India, such as
Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, and
Kerala, for collecting data. The selection of these five states
was based on the internet penetration data available for
Indian states (The Times of India, 2023). A sample size of
385 was found to be the sufficient minimum necessary sam-
ple size with a 95% confidence interval and a 5% margin of
error for the unknown population under consideration
(Shete et al., 2020). We adopted two non-probability sam-
pling techniques, the convenient and snowball sampling
methods, to collect data from the digital entertainment plat-
form users. The aim of incorporating these two distinct
techniques was to ensure that the survey questionnaire
reached as many consumers as possible within the target
population. The survey, consisting of eight questions to
gather demographic information and 32 questions, each
containing four items related to eight latent variables, was
distributed to consumers via email and various social media
platforms, including WhatsApp, Facebook, Instagram,
Telegram, and X (formerly Twitter) (Couper & Miller,
2008). The authors possessed pre-existing information about
the demographic traits of individuals within each social
media group. At the end of the 6-month data collection
period, starting from February 2023 and ending in July
2023, the questionnaire received responses from 870 active
users across different digital entertainment platforms. The
descriptive examination was performed using IBM SPPS
Statistics version 26 (IBM SPSS Statistics, Armonk, NY).
The demographic statistics of the respondents are given in
(Table 2).
3.3. Data analysis
Prior to conducting the main analysis, data screening and
preparation were performed to ensure the quality of the
data. The analysis aimed to identify missing information,
uncommitted responses, outliers, data inconsistencies, and
assess normality. Fortunately, the dataset had no missing
data points (Hair et al., 2019).
3.3.1. Common method bias (CMB)
Although surveys are widely utilized in the social sciences,
they carry the potential for common method variance and
common method bias (CMB), which can undermine the
reliability and validity of the obtained empirical outcomes
(Kock et al., 2021). Harman’s single factor test (Aguirre-
Urreta & Hu, 2019) was used to detect CMB as the data was
collected using a single response method. The findings indi-
cated that a sole factor accounts for 30.8% of the total vari-
ance, falling below the 50% (Kock, 2021) threshold. This
suggests that CMB is not significant in this context.
The data distribution of the sample was evaluated by ana-
lyzing the skewness and kurtosis values for each construct.
Based on the rule of thumb, it was confirmed that the data
followed a normal distribution, as the absolute skewness val-
ues were below two and the kurtosis values were below
seven for each construct (Table 3) (Byrne, 2013; Hair et al.,
2010) To guarantee data quality and the validity of statistical
tests, the presence of potential multivariate outliers were
examined in the sample by employing Mahalanobis D
2
in
the regression model (Ghorbani, 2019). In order to maintain
the integrity and unbiased nature of the data analysis results,
the identified true outliers from the large sample were
retained (Ghorbani, 2019).
Table 3. Descriptive statistics of the variables.
N ¼870
Mean Std. deviation Skewness Kurtosis
EE 4.049 0.631 −0.235 −0.336
SI 3.172 0.754 −0.188 −0.186
HM 3.891 0.698 −0.666 1.429
EM 3.431 0.672 −0.003 0.592
HA 2.917 0.997 0.097 −0.345
TL 3.022 0.813 −0.114 0.007
BI 3.514 0.742 −0.660 1.475
AU 3.470 0.840 −0.449 0.186
Source: SPSS output.
Table 2. Sample demographic statistics (N ¼870).
Demographic factors Categories Frequency Percentage (%)
Age 15–25 440 50.6
26–35 356 40.9
36–45 65 7.5
>45 9 1
Gender Male 525 60.3
Female 341 39.2
Prefer not to say 4 0.5
Education High school 167 19.2
Graduate 320 36.8
Post Graduate 316 36.3
Doctorate and above 67 7.7
Average time spent on streaming platforms daily <1 hr 276 31.7
1–2 hr 357 41
2–4 hr 193 22.2
>4 hr 44 5.1
The average amount spent on streaming platforms monthly <200 444 51
200–500 281 32.3
500–700 67 7.7
>700 78 9
Source: SPSS output.
6 M. KURIAKOSE AND G. NAGASUBRAMANIYAN
In this study, the partial least square–structural equation
modeling (PLS-SEM) approach with SmartPLS 4 software
was employed to investigate the proposed model. The PLS
algorithm served as the main estimation method, effectively
addressing the study’s distinct data and research context
characteristics (Hair et al., 2021a, 2021b; Sarstedt et al.,
2014). PLS-SEM is well-suited for handling deviations from
normality, managing small sample sizes, and elucidating
complex relationships among multiple latent variables. The
algorithm is in line with the study’s aim of identifying sig-
nificant relationships among constructs by maximizing the
explained variance of dependent variables within the pro-
posed research model (Beebe et al., 1998; Cassel et al., 1999;
Hair et al., 2014).
The primary analysis comprises two main stages: first,
the assessment of the measurement model and second, the
examination of the structural model. During the initial stage,
the focus lies on evaluating the discriminant validity and
reliability of the constructs. On the other hand, the subse-
quent step involves evaluating the structural relationships
between the constructs. The advantage of using PLS-SEM is
its capability to efficiently handle both reflective and forma-
tive measurement models, including single-item constructs,
without encountering identification issues (Hair et al., 2014).
4. Results
4.1. Measurement model
To test the measurement model, an examination of outer
loading composite reliability (CR), average variance
extracted (AVE), and discriminant validity was carried out
(Hair et al., 2019). The CR and Cronbach’s alpha surpass
the threshold of 0.7, signifying a strong level of internal con-
sistency (Taber, 2018) (Table 4).
The AVE value greater than 0.5 demonstrates the pres-
ence of convergent validity (Sarstedt et al., 2014).
Discriminant validity measures the degree to which a test
accurately assesses the concept that it is intended to evalu-
ate. When measures of concepts that are not expected to be
strongly related showed only low correlations, it confirms
discriminant validity (Hubley, 2014). The Fornell and
Larcker’s criterion (Table 5) and the Heterotrait–Monotrait
ratio (HTMT) (Table 6) were the two measures employed to
confirm the discriminant validity. Each construct’s square
root of the AVE value must be greater than the correlation
it has with other constructs in order to meet the require-
ment for discriminant validity (Hilkenmeier et al., 2020).
Adhering to the rule of thumb, the HTMT ratio values are
significantly lower than the 0.85 threshold (Ab Hamid et al.,
2017). Thus, the results confirm the discriminant validity of
the constructs. Table A1 in the Appendix represents the
items and the respective factor loadings.
4.2. Structural model analysis
Once the reliability and validity of the proposed model were
confirmed, the assessment of the structural model was car-
ried out to investigate the relationships between the con-
structs within the suggested model. Identifying collinearity
problems is an important prerequisite prior to engaging in
hypothesis testing. To detect collinearity, the variance infla-
tion factor (VIF) (O’Brien, 2007) values for each hypothesis
is considered. The VIF values of each hypothesis were con-
firmed to be less than the threshold value of 3.3 (Hair et al.,
2021a, 2021b) (Table 7). As a result, it can be deduced that
there is no presence of multicollinearity concern within the
dataset.
The structural model is assessed by computing the path
coefficients, t-values, and the coefficient of determination.
Using a bootstrapping method involving 5000 re-samples,
this procedure is executed while considering a 95% confi-
dence interval for the sample distribution (Hair et al., 2021a,
2021b; Streukens & Leroi-Werelds, 2016). The structural
model is shown in Figure 2.
Behavioral Intention is significantly affected by EE (b¼
0.096, t ¼3.416, p <0.05), HM (b¼0.191, t ¼4.98,
p <0.05), EM (b¼0.142, t ¼3.798, p <0.05), TL (b¼
0.12, t ¼4.506, p <0.05), and HA (b¼0.46, t ¼16.956,
p <0.05), thus supporting the hypotheses H1a, H3a, H4a,
H5a, and H6a. However, SI (b ¼− 0.2, t ¼0.682,
p >0.05) has no significant influence on behavioral inten-
tion. Thus, the hypothesis H2a is rejected. AU was signifi-
cantly predicted by behavioral intention (b¼0.748,
t ¼41.419, p <0.05). This indicates that hypothesis H7 is
Table 4. Construct reliability and validity.
Constructs
Cronbach’s
alpha
Composite
reliability (CR)
Average variance
extracted (AVE)
AU 0.808 0.875 0.637
BI 0.858 0.904 0.702
EE 0.811 0.871 0.630
SI 0.711 0.798 0.501
HM 0.861 0.905 0.706
EM 0.775 0.852 0.593
TL 0.817 0.878 0.643
HA 0.878 0.916 0.731
Table 5. Fornell–Larcker criterion.
AU BI EE EM HA HM TL SI
AU 0.799
BI 0.757 0.838
EE 0.398 0.392 0.794
EM 0.545 0.558 0.377 0.770
HA 0.702 0.661 0.264 0.442 0.855
HM 0.556 0.55 0.499 0.676 0.399 0.840
TL 0.397 0.431 0.245 0.457 0.366 0.313 0.802
SI 0.315 0.32 0.227 0.379 0.365 0.319 0.296 0.708
The off-diagonal values (highlighted in bold) represent the square root of the
AVE values.
Table 6. HTMT ratio.
AU BI EE EM HA HM TL SI
AU
BI 0.837
EE 0.463 0.43
EM 0.664 0.657 0.417
HA 0.824 0.75 0.286 0.525
HM 0.667 0.633 0.565 0.813 0.455
TL 0.473 0.495 0.274 0.565 0.425 0.355
SI 0.355 0.338 0.25 0.493 0.479 0.371 0.358
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 7
supported. Age (b¼0.077, t ¼3.74, p <0.05) has a signifi-
cant controlling effect on AU. The positive coefficient (0.077)
suggests that, as age increases, consumers’ actual usage of
digital entertainment platforms tends to increase (Benitez et al.,
2020). Gender (b ¼− 0.037, t ¼1.54, p >0.05) and educa-
tion (b¼0.026, t ¼0.288, p >0.05) have no significant con-
trolling effect on AU. Table 7 summarizes the path analysis
and the hypothesis test results.
4.2.1. Specific indirect effect
The indirect pathways running from EE through behavioral
intention to AU (b¼0.072, t ¼3.398, p <0.05), from HM
through behavioral intention to AU (b ¼0.143, t ¼4.923,
p <0.05), from EM to behavioral intention to AU (b¼
0.106, t ¼3.763, p <0.05), from TL through behavioral
intention to AU (b¼0.09, t ¼4.394, p <0.05) and from
HA through behavioral intention to AU (b¼0.344, t ¼15.
343, p <0.05) are significant. These results support the
hypotheses H1b, H3b, H4b, H5b, and H6b. However, the
indirect pathway from SI through behavioral intention to
AU (b ¼− 0.015, t ¼0.681, p >0.05) does not show any
significance. Thus, the hypothesis H2b is rejected. Table 8
summarizes the hypothesis test results.
The analysis included the assessment of R
2
(coefficient of
determination) for in-sample explanatory capability, and Q
2
Table 7. Path analysis.
H Structural path Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (jO/STDEVj)p Values
H1a EE ->BI 0.096 0.096 0.028 3.416 0.000
H2a SI ->BI −0.020 −0.016 0.029 0.682 0.248�
H3a HM ->BI 0.191 0.191 0.038 4.980 0.000
H4a EM ->BI 0.142 0.142 0.037 3.798 0.000
H5a TL ->BI 0.120 0.12 0.027 4.506 0.000
H6a HA ->BI 0.460 0.459 0.027 16.956 0.000
H7a BI ->AU 0.748 0.748 0.018 41.419 0.000
Age ->AU 0.077 0.077 0.021 3.740 0.000
Gender ->AU −0.037 −0.038 0.024 1.540 0.062�
Education ->AU 0.026 0.027 0.025 1.064 0.144�
�p >0.05, the hypothesis is rejected.
Figure 2. Structural model.
8 M. KURIAKOSE AND G. NAGASUBRAMANIYAN
predict (predictive significance) to evaluate the effectiveness
of the structural model in explaining the data within the
sample and its ability to predict outcomes. Q-square values
greater than zero suggest an effective reconstruction of the
values and indicate the model’s predictive relevance (Fauzi,
2022). As shown in Table 9, the model explains 58. 1% of
the variance in AU and 56. 9% of the variance in behavioral
intention. The result concludes that the model has signifi-
cant explanatory power.
The assessment of predictive significance was conducted
using PLSpredict. The Q-square predict values for both AU
(0.563) and behavioral intention (0.562) are greater than
zero, suggesting that the model holds predictive validity
(Table 10).
5. Discussion
The objective of this study was to investigate the factors
influencing consumers’ behavioral intention to adopt digital
entertainment platforms for accessing entertainment content,
consequently leading to the effective utilization of these
digital platforms. The objective has been achieved by
expanding the UTAUT2 framework by incorporating two
novel variables: EM and TL. The precise intention behind
introducing EM and TL into the model is to investigate
whether individuals utilizing digital entertainment platforms
are actively pursuing meaningfulness and Peacefulness in
their choices concerning the adoption of digital technology
for entertainment consumption.
5.1. Effort Expectancy (H1a, H1b)
The findings corroborated the significance of EE in predict-
ing the behavioral intentions related to the adoption of
digital entertainment platforms. This outcome aligns with
prior studies conducted in diverse geographic and
demographic settings that have also emphasized the impor-
tance of EE (Nagaraj et al., 2021; Singh et al., 2021). The
significant predictability to forecast user behavior concern-
ing EE indicates that individuals perceive digital entertain-
ment platforms as convenient and simple. Furthermore, the
user-friendly interface enhances its appeal to consumers.
5.2. Hedonic motivation (H3a, H3b)
The pleasure-seeking aspects of consumer behavior are evi-
dent when examining the adoption of digital entertainment
platforms for entertainment consumption. The significant
impact of HM on the behavioral intention to adopt digital
entertainment platforms reveals that consumers find digital
platforms enjoyable and pleasurable. The findings of this
study agree with the existing literature regarding pleasure-
oriented consumer behavior toward video-on-demand plat-
forms and OTT platforms (Bhattacharyya et al., 2022; Singh
et al., 2021; Malewar & Bajaj, 2020; Mulla, 2022).
5.3. Eudemonic motivation (H4a, H4b)
This research further reveals that EM, which contrasts with
HM (Ewert et al., 2020), has a significant role in predicting
the behavioral intention to adopt digital entertainment plat-
forms. According to consumers, digital entertainment plat-
forms offer a valuable and enriching streaming experience
that contributes to a sense of meaningfulness, satisfaction,
and personal development for users. Along with enjoyment,
a meaningful viewer experience is also an outcome of adopt-
ing digital entertainment platforms. The preferences revealed
by the results of this study correspond with the insights pro-
vided by previous research on leisure activities (Ewert et al.,
2020; Salerno, 2009), i.e., the motivation-oriented elements
of consumer behavior toward engaging in different enter-
tainment activities.
5.4. Tranquility (H5a, H5b)
Further, the role of TL in measuring the behavioral inten-
tion to adopt digital entertainment platforms has a signifi-
cant result. The results validate that individuals perceive
digital entertainment platforms as beneficial for experiencing
peace and serenity. Furthermore, engaging in streaming
services contributes to relaxation and aids in alleviating
mental distress. Within the realm of tourism studies, the act
of tourists seeking out serene destinations can be understood
as their aspiration to attain a state of TL through a change
Table 8. Specific indirect effect.
H Structural Path Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (jO/STDEVj)p Values
H1b EE ->BI ->AU 0.072 0.072 0.021 3.398 0.000
H2b SI ->BI ->AU −0.015 −0.012 0.022 0.681 0.248�
H3b HM ->BI ->AU 0.143 0.143 0.029 4.923 0.000
H4b EM ->BI ->AU 0.106 0.106 0.028 3.763 0.000
H5b TL ->BI ->AU 0.09 0.09 0.02 4.394 0.000
H6b HA ->BI ->AU 0.344 0.344 0.022 15.343 0.000
�p >0.05, the hypothesis is rejected.
Table 9. Coefficient of determination.
R-square R-square adjusted
AU 0.583 0.581
BI 0.572 0.569
Table 10. Predictive significance.
Q
2
predict RMSE MAE
AU 0.563 0.663 0.510
BI 0.562 0.664 0.512
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 9
of environment (Hu et al., 2021). In contrast to displacing
oneself for a tranquil experience, digital entertainment plat-
forms help achieve inner TL without physically relocating
oneself to a different place.
5.5. Habit (H6a, H6b)
The findings further indicated that the influence of HA as a
predictive factor significantly affects consumers’ intentions
to adopt digital entertainment platforms. Utilizing digital
platforms for entertainment has evolved into a habitual
practice for users. These outcomes align with prior studies
that investigated the role of HA in shaping consumption
choices related to entertainment content distributed through
OTT platforms (Malewar & Bajaj, 2020).
5.6. Social influence (H2a, H2b)
The outcomes of this study reveal that the impact of SI on the
adoption of digital entertainment platforms is not significant.
This result agrees with the findings of Malewar and Bajaj
(2020) in their research regarding OTT platforms. On the con-
trary, Shah and Mehta (2022) have a different finding about
the significance of SI on the intentions to adopt OTT plat-
forms, i.e., the existence of a positive significance of SI. These
conflicting findings can be clarified by considering that the
choice to utilize digital entertainment platforms is primarily
driven by personal decisions rooted in individual psychological
motivations and interests rather than being strongly swayed by
the influence of social circles, celebrities, or family members.
6. Conclusion
6.1. Theoretical contribution
This study holds substantial relevance in adding to the exist-
ing literature concerning the adoption of technology, par-
ticularly the digitalization of entertainment media platforms
and their acceptance by consumers. There exists remarkable
research extending the UTAUT2 model with different varia-
bles to study the consumers’ behavioral intention toward
adopting digital platforms, including digital entertainment
platforms, specifically OTT platforms. In this research, the
approach toward the UTAU2 model was slightly different. It
was not a random selection of variables to study the user
behavior; instead, it was an approach to explore whether the
consumers seek meaningful consumption along with a tran-
quil experience in adopting a technology to access entertain-
ment. The novelty of this research is that the available
research regarding digital entertainment platforms does not
sufficiently explain the human desire to find meaningfulness
in spending on digital entertainment platforms.
The human–computer interaction is often considered
mechanical. But humans are wired to be emotional, moti-
vated, and constantly seeking a tranquil state of mind. The
advancement of behavioral economics, leading a pathway
toward the amalgamation of economics-psychology theories
to explain consumption behavior in a better realistic manner,
has opened the door to various methodological approaches to
studying consumer behavior regarding such mechanical inter-
actions. This research has adopted two psychological variables
to study consumer behavior toward digital entertainment
platforms. The significant findings demonstrate that the pur-
suit of eudemonic well-being and the desire for a peaceful
and calming experience are among the numerous important
factors driving the acceptance of digital entertainment plat-
forms. Furthermore, these motivations prompt users to inte-
grate these platforms into everyday routines.
6.2. Practical implications
The demand for entertainment continues to expand cease-
lessly. Technological progress has brought numerous disrup-
tions in creating, distributing, and consuming entertainment
content. Among these changes, digitalization represents the
most recent transformation in the media and entertainment
sector. Individuals’ trajectory in response to these disrup-
tions holds the key to the industry’s future. Given this scen-
ario, it becomes pertinent to comprehend the diverse factors
that shape consumers’ choices while adopting new technolo-
gies for accessing entertainment.
The influence of motivational and well-being aspects of
consumer behavior in choosing digital entertainment plat-
forms is revealed by studying the EM and desire for a tran-
quil experience. The time and location limitations can
confine a person into his own home instead of traveling to
serene locations to experience TL. In such situations, digital
entertainment platforms facilitate a personalized viewing
experience to the users by providing their favorite entertain-
ment content online. This viewing mode distinguishes itself
from traditional modes due to features like personalized
content offerings and the ability to binge-watch.
Further, the findings obtained from this research can pos-
sibly assist enterprises within the entertainment sector, con-
tent creators, and streaming services in enhancing their
product offerings. They have the potential to customize the
content, pricing structures, and user interactions according to
consumer inclinations. This, in turn, can result in heightened
subscription rates, viewership levels, and financial returns.
6.3. Limitations and scope for future research
While this study’s significant findings hold various valuable
implications, it is crucial to acknowledge certain limitations.
Specifically, these limitations pertain to the sample’s scope,
which is confined to the southern states of India, and the omis-
sion of moderating factors such as age, gender, and experience
that are integral components of the original UTAUT2 model.
Also, future research covering the samples across India and
cross-country comparisons can provide more insights into con-
sumer behavior toward digital entertainment platforms.
Acknowledgment
We sincerely thank Mr. Arun Kumar P. (NIT Tiruchirappalli) for his
valuable data analysis advice.
10 M. KURIAKOSE AND G. NAGASUBRAMANIYAN
Disclosure statement
There is no conflict of interest expressed by the author(s).
ORCID
Mary Kuriakose http://orcid.org/0000-0002-2376-480X
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About the authors
Mary Kuriakose is a Ph.D. research scholar in Economics at the
National Institute of Technology Tiruchirappalli, focusing on consumer
behavior and the influence of emerging media, especially digital enter-
tainment platforms. Her research spans behavioral economics, digital
entertainment consumption, and market choices driven by public
sentiments.
Gopalan Nagasubramaniyan, an Associate Professor and Research
Supervisor at the National Institute of Technology Tiruchirappalli, spe-
cializes in entrepreneurship development and Economics. His research
contributions have been featured in esteemed peer-reviewed journals,
and he has also collaborated as a co-author on a book titled
“Engineering Economics and Management.”
14 M. KURIAKOSE AND G. NAGASUBRAMANIYAN
Appendix
Table A1. Items of the constructs.
Item Description Factor loading
EE1 “Learning to use digital entertainment platforms for streaming content is easy.” 0.693
EE2 “My interaction with the digital entertainment platforms is clear and understandable.” 0.865
EE3 “I think the digital entertainment platforms are simple.” 0.804
EE4 “It is easy to become skillful at using the digital entertainment platforms.” 0.803
SI1 “The social groups I am a part of influence my decisions to subscribe to digital entertainment platforms.” 0.707
SI2 “The influential people’s opinions affect how I use digital entertainment platforms.” 0.714
SI3 “I value the suggestions and opinions of my friends about digital entertainment platforms.’ 0.813
SI4 “The opinions of celebrities about digital platforms’ original shows matter to my viewing preferences.” 0.576
HM1 “I find using digital entertainment platforms is fun.” 0.809
HM2 “I find using digital entertainment platforms is enjoyable.” 0.89
HM3 “I find using digital entertainment platforms is entertaining.” 0.825
HM4 “I find using digital entertainment platforms is pleasurable.” 0.833
EM1 “Watching movies and series from digital entertainment platforms makes me feel good.” 0.804
EM2 “I find accessing digital entertainment platforms content is meaningful.” 0.734
EM3 “Accessing entertainment content through digital entertainment platforms is fulfilling.” 0.825
EM4 “I find entertainment content on OTT platforms increases my wisdom.” 0.671
TL1 “I get a peaceful feeling from digital entertainment platforms.” 0.742
TL2 “I get relaxation from digital entertainment platforms.” 0.847
TL3 “A sense of calmness I feel by watching certain shows from digital entertainment platforms.” 0.845
TL4 “I use digital entertainment platforms to create a serene environment when I feel stressed.” 0.769
HA1 “Digital entertainment platforms have become a habit for me.” 0.843
HA2 “I cannot think about not using digital entertainment platforms to access entertainment content.” 0.823
HA3 “I feel like I must use digital entertainment platforms.” 0.875
HA4 “Using digital entertainment platforms became natural for me.” 0.879
BI1 “I intend to use digital entertainment platforms for entertainment.” 0.836
BI2 “I plan to engage in the digital entertainment platforms routinely.” 0.865
BI3 “I predict using the digital entertainment platforms next time I want to watch entertainment.” 0.854
BI4 “I plan to use the digital entertainment platforms when entertainment is needed.” 0.795
AU1 “I regularly use digital entertainment platforms for accessing entertainment.” 0.853
AU2 “Using digital entertainment platforms is a pleasant experience.” 0.715
AU3 “I currently use digital entertainment platforms more than using other traditional media such as television.” 0.83
AU4 “I spend a lot of time on digital entertainment platforms watching entertainment content online.” 0.787
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 15
